Bayesian phylogeography of influenza A/H3N2 for the 2014-15 season in the United States using three frameworks of ancestral state reconstruction.
Bayesian phylogeography of influenza A/H3N2 for the 2014-15 season in the United States using three frameworks of ancestral state reconstruction.
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DOI：
10.1371/journal.pcbi.1005389
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发表时间：
2017-02
影响因子：
4.3
通讯作者：
Scotch M
中科院分区：
文献类型：
--
作者：
Magee D;Suchard MA;Scotch M
利用贝叶斯随机搜索变量选择（BSSVS）框架，改进了病毒流行病贝叶斯地理学中的祖先状态重建。最近，这个框架已被扩展到离散状态之间的转换率矩阵作为一个广义线性模型（GLM）的遗传，地理，人口和环境的预测感兴趣的病毒，并纳入BSSVS估计后验包含概率的每个预测。虽然后者似乎增强了祖先国家重建的生物学有效性，但还没有对这两种方法所创造的遗传学进行比较。在本文中，我们比较了这两种方法，同时也使用了一个原始的方法没有BSSVS，并强调了每一个创建的同源性的差异。我们测试了2014-15年美国流感季节期间H3 N2流感的六个合并先验和六个随机序列样本。我们表明，GLM在六个先验中的五个先验下产生的根状态后验概率显著高于两种替代方法，并且在所有先验下产生的Kullback-Leibler发散值显著高于两种替代方法。此外，GLM强烈暗示温度和降水是这个流感季节的驱动力，并且几乎一致地确定了一个单一的根状态，该状态在美国典型的流感季节期间表现出最热带的气候。然而，与其他方法相比，GLM似乎非常容易受到采样偏差的影响，这让人怀疑它的重建是否应该比其他方法创造的更受欢迎。我们报告说，一个BSSVS的方法与泊松先验证明在某些条件下比GLM或原始模型对样本量的偏差较小，并认为重建方法和采样偏差之间的联系值得进一步研究。在过去十年的大部分时间里，流行病学研究人员采用贝叶斯框架来重建系统发育树，并确定病毒分支之间的时空关系。最近，这一框架的扩展使人们能够直接评估各种人口、地理、遗传和环境变量在这些关系中发挥的作用，但还没有对前者和后者进行比较。在这里，我们的目标是评估两种重建技术之间的差异，以及额外的原始方法，使用2014-15年美国流感季节作为各种人口增长情景下的案例研究。我们强调了新方法如何证明常见的流感趋势显着增加，以及该方法确定的气候预测因子似乎与流感季节性趋势的已知趋势一致。然而，我们发现这种方法似乎受病毒获得地点的影响最大。我们的工作为希望研究病毒进化历史的研究人员提供了有价值的见解，也可能有助于确定为特定的病毒地理学应用选择正确的方法。
Ancestral state reconstructions in Bayesian phylogeography of virus pandemics have been improved by utilizing a Bayesian stochastic search variable selection (BSSVS) framework. Recently, this framework has been extended to model the transition rate matrix between discrete states as a generalized linear model (GLM) of genetic, geographic, demographic, and environmental predictors of interest to the virus and incorporating BSSVS to estimate the posterior inclusion probabilities of each predictor. Although the latter appears to enhance the biological validity of ancestral state reconstruction, there has yet to be a comparison of phylogenies created by the two methods. In this paper, we compare these two methods, while also using a primitive method without BSSVS, and highlight the differences in phylogenies created by each. We test six coalescent priors and six random sequence samples of H3N2 influenza during the 2014–15 flu season in the U.S. We show that the GLMs yield significantly greater root state posterior probabilities than the two alternative methods under five of the six priors, and significantly greater Kullback-Leibler divergence values than the two alternative methods under all priors. Furthermore, the GLMs strongly implicate temperature and precipitation as driving forces of this flu season and nearly unanimously identified a single root state, which exhibits the most tropical climate during a typical flu season in the U.S. The GLM, however, appears to be highly susceptible to sampling bias compared with the other methods, which casts doubt on whether its reconstructions should be favored over those created by alternate methods. We report that a BSSVS approach with a Poisson prior demonstrates less bias toward sample size under certain conditions than the GLMs or primitive models, and believe that the connection between reconstruction method and sampling bias warrants further investigation. For the better part of the last decade, epidemiological researchers have employed a Bayesian framework to reconstruct phylogenetic trees and determine the spatiotemporal relationships between clades of viruses. Recently, an extension of this framework has enabled direct assessment of how various demographic, geographic, genetic, and environmental variables play a role in these relationships, but there has yet to be a comparison between the former and the latter. Here, we aim to assess the differences between the two reconstruction techniques, as well as an additional primitive method, using the 2014–15 influenza season in the U.S. as a case study under a variety of population growth scenarios. We highlight how the new method demonstrates significant increases in commonly-reported trends in phylogenies and that the method identifies climate predictors that appear to be consistent with known trends in seasonal trends in influenza. However, we found that this method appears to be the most heavily influenced by the locations at which the viruses were obtained. Our work offers valuable insight for researchers wishing to study the evolutionary history of viruses and also may prove useful in determining the correct method to choose for a given application of virus phylogeography.